---
title: "CXCL10 rs8878 genotype associates with preserved T cells and 30‑day survival in sepsis"
id: "frontiers-in-immunology-1-cxcl10-rs8878-identifies-a-genotype-associated-immune-phenotype-linked-to-t"
canonical_url: "https://medichelpline.com/clinical-feed/frontiers-in-immunology-1-cxcl10-rs8878-identifies-a-genotype-associated-immune-phenotype-linked-to-t"
content_type: "clinical_feed_article"
specialty: "Infectious Disease"
source_name: "Frontiers in Immunology"
source_url: "https://www.frontiersin.org/articles/10.3389/fimmu.2026.1887361"
published_at: "2026-07-17T00:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# CXCL10 rs8878 genotype associates with preserved T cells and 30‑day survival in sepsis
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/frontiers-in-immunology-1-cxcl10-rs8878-identifies-a-genotype-associated-immune-phenotype-linked-to-t
- **Specialty:** [Infectious Disease](https://medichelpline.com/clinical-feed/infectious-disease.md)
- **Primary Source:** Frontiers in Immunology
- **Source URL:** [Original Journal Publication](https://www.frontiersin.org/articles/10.3389/fimmu.2026.1887361)
- **Published At:** 2026-07-17T00:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- This prospective multicenter cohort study evaluated the impact of the CXCL10 3′UTR single nucleotide polymorphism **rs8878** on immune phenotype and outcome in sepsis. - A total of 278 septic patients were genotyped; 30‑day survival data were available for 252 patients. Flow cytometry was performed in a subset (n=145) and whole‑blood RNA analyses in 131 patients. Proteomics analyses used a cohort of 252 with propensity matching for AA vs GG/AG. - Carriers of the **rs8878 AA genotype** had higher circulating total T cell counts and showed improved 30‑day survival compared with G‑allele carriers (AG/GG). - Higher total and **CD8+ T cell** counts on day 1 were significantly associated with better survival across the cohort. - Among non‑survivors, AA‑genotype carriers demonstrated increased CXCL10 mRNA expression, indicating genotype‑dependent regulation of CXCL10 in fatal disease progression. - Day‑1 CXCL10 concentrations correlated positively with multiple inflammatory cytokines (IL‑6, IL‑8, IL‑10, IL‑18, MCP‑1, IFN‑γ, IFN‑α2) and inversely with total T cell counts, supporting a link between **CXCL10**, systemic inflammation, and T cell depletion. - No significant differences were observed between genotypes in plasma proteomics or routine clinical parameters after matching and multiple‑testing correction. - The findings suggest the **CXCL10 rs8878** variant contributes to inter‑individual differences in adaptive immune responses during sepsis and may serve as a biomarker for risk stratification or a candidate for immunomodulatory targeting. - Details on effect sizes, cutoff values, and mechanistic causality were not reported in the source beyond the associations summarized above.
## Clinical Analysis & Structured Key Points
About us All journals All articles Submit your research Search Login Frontiers in Immunology Sections Articles Research Topics Editorial board About journal Published in Frontiers in Immunology Cytokines and Soluble Mediators in Immunity 7 impact factor 11.3 citescore Part of a Research Topic Transcriptional regulation of cytokines in health and disease Submission open 1819 views 2 articles Editor & Reviewers Edited by Athanasia Mouzaki Reviewed by Collins Boahen Grace Fisler Outline Abstract 1 Introduction 2 Materials and methods 3 Results 4 Discussion Data availability statement Ethics statement Author contributions Funding Acknowledgments Conflict of interest Generative AI statement Publisher’s note Supplementary material References Figures and Tables Figure 1 View in article Figure 2 View in article Figure 3 View in article Figure 4 View in article Table 1 Cohort description, classification according to rs8878 SNP AA and GG/AG-genotype. View in article Table 2 Distribution of circulating immune cell counts stratified by CXCL10 rs8878 genotype on day 1 of sepsis. View in article Table 3 Multivariate COX-regression of CD8+-cell count cutoff. View in article ORIGINAL RESEARCH article Front. Immunol., 17 July 2026 Sec. Cytokines and Soluble Mediators in Immunity Volume 17 - 2026 | https://doi.org/10.3389/fimmu.2026.1887361 CXCL10 rs8878 identifies a genotype-associated immune phenotype linked to T-lymphocyte preservation and survival in sepsis Birte Dyck 1† Andrea Witowski 1,2† Thilo Bracht 3 Malte Bayer 3 P T Patrick Thon 1 D Z Dominik Ziehe 1 Tim Rahmel 2 M U Matthias Unterberg 2 B W Britta Westhus 2 L P Lars Palmowski 2 H N Hartmuth Nowak 2,4 Stefan Felix Ehrentraut 5 J O Jennifer Orlowski 1 A V Alexander von Busch 2 Alexander Zarbock 6 Nina Babel 7 Moritz Anft 7 D H Dietrich Henzler 8 M A Michael Adamzik 2 Lars Bergmann 2 B S Barbara Sitek 3 Björn Koos 1 +14 more Katharina Rump 1* 1. Department of Anesthesiology, Intensive Care Medicine and Pain Therapy, Center of perioperative precision medicine, Ruhr University Bochum, Knappschaft Kliniken University Hospital Bochum, Bochum, Germany 2. Ruhr University Bochum, Knappschaft Kliniken University Hospital Bochum, Department of Anesthesiology, Intensive Care Medicine and Pain Therapy, Bochum, Germany See more Article metrics View details 267 Views Abstract Background: Sepsis is characterized by a dysregulated host response to infection, leading to concurrent hyperinflammation and immunosuppression, including profound alterations in T lymphocyte homeostasis. The chemokine CXCL10, an interferon-γ-inducible mediator of T cell trafficking, has been implicated in immune activation and tissue injury. However, it remains unclear whether genetic variation in CXCL10 contributes to T cell dysregulation and clinical outcomes in sepsis. Methods: In a prospective cohort of septic patients (n=278), we analyzed CXCL10 rs8878 genotypes, circulating immune cell counts, cytokine concentrations, and CXCL10 protein and mRNA expression in whole blood. Associations between genotype, immune parameters, plasma proteomics and 30-day survival were assessed using group comparisons and Kaplan-Meier analyses. Correlation analyses were performed to evaluate relationships between CXCL10 concentrations, cytokines, and clinical parameters. Results: Variants in the CXCL10 gene were associated with T cell dysregulation. Carriers of the rs8878 AA genotype exhibited higher circulating T cell counts and improved survival compared with G-allele carriers. Higher total and CD8+ T cell counts were significantly associated with improved survival. Among non-survivors, AA-genotype carriers showed increased CXCL10 mRNA expression, indicating genotype-dependent regulation of CXCL10 expression under conditions of fatal disease progression. CXCL10 concentrations on day 1 were positively correlated with multiple inflammatory cytokines, including IL-6, IL-8, IL-10, IL-18, MCP-1, IFN-γ, and interferon-α2, and inversely correlated with total T cell counts, supporting a link between CXCL10, systemic inflammation, and T cell depletion. No significant associations were observed between CXCL10 genotype and plasma proteomics and routine clinical parameters. Conclusion: The CXCL10 rs8878 genotype is associated with T cell dynamics and 30-day survival in sepsis, suggesting a genotype-dependent modulation of the adaptive immune response. While the AA genotype is linked to preserved T cell counts and improved outcomes, increased CXCL10 expression in non-survivors points to a context-dependent role in inflammation-driven immune dysregulation. These findings identify CXCL10 as a potential biomarker for risk stratification and a candidate target for immunomodulatory therapies in sepsis. 1 Introduction Sepsis is a life-threatening syndrome resulting from a dysregulated host response to infection and remains a major cause of morbidity and mortality in critically ill patients worldwide (1). During sepsis, profound alterations occur in both the innate and adaptive immune system (2). A hallmark of sepsis is immune dysfunction, including profound alterations in adaptive immunity (3). Among immune cells, T lymphocytes (T cells) play a central role in orchestrating pathogen clearance and maintaining immune homeostasis (3). Reduced circulating T cell counts during sepsis have been consistently associated with impaired immune competence and worse clinical outcomes (4). Alterations in T cell numbers and function during sepsis are closely linked to dysregulated chemokine signaling, particularly involving CXCL10 (C-X-C motif chemokine ligand 10; also known as interferon-γ-induced protein 10, IP-10). Chemokines, together with acute-phase reactants, are among the key molecular mediators associated with sepsis. In septic patients, several chemokines of the CC motif family measured in serum or plasma samples(including CCL1, CCL2, CCL8, and CCL20) as well as CXC motif chemokines (such as CXCL8, CXCL10, and CXCL12), along with various cytokines, are significantly increased compared to healthy controls (5). CXCL10 is a chemokine induced by interferon-γ (IFN-γ) and other pro-inflammatory cytokines and mediates T cell trafficking to sites of inflammation (6). It binds to the receptor CXCR3, which is highly expressed on activated CD8+ cytotoxic T cells, Th1 CD4+ T cells, and NK cells (6). Elevated CXCL10 levels have been observed in septic patients, and its expression has been linked to both immune activation and organ injury (7). They have therefore been associated with disease severity, progression to septic shock, and poor outcome, although no consistent cut-off value has been established and concentrations vary depending on timing and methodology (8). As mentioned above, sepsis is also characterized by profound T cell lymphopenia, and while a direct clinical correlation between CXCL10 levels and absolute T cell counts has not been consistently demonstrated, experimental and immunological evidence suggests that high CXCL10 levels may promote CXCR3-dependent T cell redistribution, exhaustion, and apoptosis, thereby contributing to sepsis-associated immunosuppression (8). Variations in CXCL10 expression and function have been implicated in several infectious, inflammatory, and malignant diseases (9, 10). Investigating single nucleotide polymorphisms (SNPs) in the CXCL10 gene offers insight into inter-individual differences in immune response, disease susceptibility, progression, and outcome (11). Several studies show associations between CXCL10 promoter or untranslated region polymorphisms and disease. Among these variants, the 3′ untranslated region SNP rs8878 has been associated with altered CXCL10 expression and clinical outcomes in inflammatory diseases, including rheumatoid arthritis (12). Therefore, rs8878 represents a biologically plausible candidate variant for investigating the impact of CXCL10 genetics on immune dysregulation and outcome in sepsis. However, the impact of CXCL10 genetic variation on T cell abundance and survival in sepsis has not been fully elucidated. Despite extensive research on the role of T cells and CXCL10 in sepsis, there is limited evidence connecting CXCL10 genotype, adaptive immune response, and clinical outcome. Understanding this relationship could provide mechanistic insights into the immune regulation in sepsis and identify potential prognostic biomarkers. Therefore, in this study, we aimed to investigate the association between CXCL10 rs8878 genotype, circulating T lymphocyte counts, and 30-day survival outcomes in septic patients. Additionally, we assessed CXCL10 mRNA expression in non-survivors to explore potential genotype-dependent mechanisms underlying immune response and mortality. We hypothesized that the CXCL10 rs8878 polymorphism contributes to inter-individual differences in the immune response during sepsis and that AA-genotype carriers would exhibit enhanced preservation of circulating T-cell populations, altered CXCL10 expression, and improved 30-day survival compared with AG/GG genotype carriers. 2 Materials and methods 2.1 Study design and conceptual overview In total, 278 patients from the prospective, multicenter SepsisDataNet.NRW cohort (185 patients from clinic A, 32 patients from clinic B, 18 patients from clinic C, 22 patients from clinic D, 14 patients from clinic E, 4 patients from clinic F and 3 patients from clinic G) were included. Clinical data, blood samples, and follow-up information were collected prospectively according to the study protocol. The analyses presented in the current manuscript, including genotyping, flow cytometry, and gene expression analyses, were subsequently performed using the collected biospecimens and data. Patients were recruited consecutively between March 1, 2018, and May 31, 2022. Systematic screening ensured that all eligible ICU patients were considered for inclusion. Written consent was obtained from all patients or their legal guardians. This study was approved by the Ethics Committee of the Medical Faculty of the Ruhr-University of Bochum (Registration no. 19-6606 3-BR). All research involving human participants was conducted in full accordance with the Declaration of Helsinki, as well as institutional and national guidelines and regulations. The study strictly adhered to the protocols outlined in the approved ethics vote. Intensive care patients aged 18 and older were eligible for recruitment if they met the current Sepsis-3 criteria for sepsis diagnosis (1). Individuals admitted with suspected or confirmed infection who did not initially meet the criteria for sepsis were not enrolled until sepsis-associated organ dysfunction developed. To enhance the generalizability of our findings and account for the heterogeneity of sepsis progression, the study protocol allowed for patient inclusion within 36 hours of sepsis diagnosis. This ensured that patients initially treated on the general ward before ICU transfer were not systematically excluded. For patients diagnosed with sepsis upon ICU admission, enrollment and sample collection were performed immediately to capture the earliest possible disease stage. Treatment of patients was carried out according to the current national and international guidelines and was not influenced by participation in the study. Blood samples for DNA, RNA, and serum analysis were collected within the first 36 hours after diagnosis and stored at -80 °C after initial processing. Blood samples for FACS analysis where freshly prepared and directly analyzed. A total of 278 patients with sepsis were successfully genotyped and were included in the genetic analyses. 30-day survival data were available for all 252 patients. Complete baseline clinical and demographic characteristics were available for 234 patients and are presented in Table 1. Flow cytometric (FACS) analyses were performed in a subset of 145 patients for whom data from freshly prepared blood samples were available. For gene expression analyses, RNA of sufficient quantity and quality was available from 131 patients. A detailed overview of patient inclusion and sample availability for each downstream analysis is provided in Supplementary Figure 1. Table 1 Characteristics AA (n= 46) GG/AG (n=188) P-value Base characteristics Female sex, n (%) 18 (40.0) 75 (40.1) 1 Age, years (IQR) 64 (21.0) 64 (21.25) 0.642 SAPS-II, day 1 (IQR) 43 (33.5-52.5) 39 (28-49) 0.225 SOFA Score, day 1 (IQR) 6.00 (4.00-9.00) 8.00 (5.00-11.00) 0.393 Length of ICU stay, days (IQR) 16 (4-28) 9 (2-16) 0.034 Body temperature day 1 (°C) 31.002 ± 0.549 37.096 ± 0.906 0.305 Heart rate (beats per minute) 79 ± 17 84 ± 19 0.060 systolic blood pressure (SBP) (mmHg) 121 ± 12 122 ± 15 0.430 diastolic blood pressure (DBP) (mmHg) 62 ± 9 61 ± 9 0.333 mean arterial pressure (MAP) (mmHg) 83 ± 9 82 ± 10 0.197 Comorbid conditions, n (%) Hypertension 28 (60.9) 118 (62.8) 0.946 Cardiovascular disease 13 (28.3) 67 (35.6) 0.440 COPD* 3 (6.5) 23 (12.2) 0.431 Other Lung disease 8 (17.4) 22 (11.7) 0.430 Diabetes mellitus 13 (28.3) 59 (31.4) 0.816 Chronic kidney disease 6 (13) 41 (21.8) 0.261 Obesity (Body mass index ≥30 kg/m²) 20 (43.5) 56 (29.8) 0.109 Organ transplantation 2 (4.3) 24 (12.8) 0.122 Malignant neoplasms 9 (19.6) 41 (21.8) 0.895 Infection focus, n (%) 0.770 pulmonary 11 (24.4) 57 (30.3) Urinary tract 3 (6.7) 11 (5.9) Abdomen 5 (11.1) 29 (15.4) Central nervous system 1 (2.2) 3 (1.6) Bloodstream 1 (2.2) 6 (3.2) COVID-19 20 (44.4) 70 (37.2) Other/Unknown 4 (8.9) 12 (6.4) Laboratory values, day 1 C-reactive protein [mg/L] 18.88 ± 14.10 15.49 ± 10.45 0.399 Procalcitonin [ng/mL] 14.06 ± 25.33 6.90 ± 13.53 0.550 Leucocytes [1000/µL] 16.33 ± 6.71 13.42 ± 7.23 0.213 Creatinine [mg/dL] 1.61 ± 1.06 1.53 ± 1.24 0.271 Bilirubin [mg/dL] 0.81 ± 0.98 0.93 ± 1.24 0.736 INF-α2 2.95 ± 2.5 2.28 ± 2.6 0.113 INF-γ 9.17 ± 9.17 6.59 ± 8.15 0.116 Cohort description, classification according to rs8878 SNP AA and GG/AG-genotype. Data are presented as n (%) and median (IQR). *Chronic obstructive pulmonary disease (COPD). Bold values show significance levels p < 0.05. 2.2 DNA genotyping DNA was isolated from EDTA-blood samples using the my-Budget Blood DNA Midi Kit (Bio-Budget Technologies GmbH, Krefeld, Germany) according to the manufacturer’s instructions as previously described. Genotyping of the CXCL10 rs8878 was performed using the Thermo Fisher Scientific TaqMan® SNP Genotyping Assay (Thermo Fisher Scientific, Wilmington, USA) and Bio-Rad CFX Connect Cycler Systems (Bio-Rad Laboratories, Inc., Hercules, USA) using a protocol of 95 °C for 10 minutes and 40 cycles of 95 °C for 15 seconds followed by 60 °C for 60 seconds. 2.3 RNA analysis RNA was extracted from whole blood collected with Tempus™ Blood RNA Tubes (Applied Biosystems, Waltham, USA) using Tempus™ Spin RNA Isolation Reagent Kits (Applied Biosystems, Waltham, USA), followed by complementary DNA (cDNA) synthesis using the High-Capacity cDNA Reverse Transcription Kit by Applied Biosystems (Applied Biosystems, Waltham, USA). RNA concentration and purity were assessed using a NanoDrop spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA). RNA samples were quantified spectrophotometrically, and purity was evaluated using the A260/A280 and A260/A230 absorbance ratios. Only RNA samples of sufficient quantity and quality were included in the subsequent gene expression analyses. Then, quantitative polymerase chain reaction was performed using our primers listed in Supplementary File 2 for expression analysis of total CXCL10 in relation to ACTB, as previously described (13). The protocol used with GoTaq® qPCR MasterMix (Promega, Madison, USA) involved 2 minutes of 95 °C followed by 40 cycles of 95 °C for 15 seconds and 60 °C for 60 seconds. 2.4 CXCL10 serum concentrations Serum samples collected in Serum Gel Z tubes (Sarstedt, Germany) were analyzed using enzyme-linked immunosorbent assay (ELISA) kits to determine CXCL10 serum levels (Human CXCL10/IP-10 Quantikine ELISA Kit, bio-techne; Wiesbaden, Germany). Samples were diluted as necessary to fall within the standard detection range of the kit. 2.5 Plasma proteomics Plasma samples were processed and analyzed as described previously by Palmowski et al. (14). Briefly, the samples were measured distributed over several batches and each batch was analyzed separately using DIA-NN (ver. 1.8.1), searching against the human SwissProt database (ver. 2022_05). Subsequently, a batch normalization procedure accompanied by quality control was carried out, resulting in log2-transformed normalized protein intensities. The resulting data set can be accessed via the PRIDE repository under the data set identifier PXD055932. The respective cohort included 252 patients of which 30 carried the AA genotype. Propensity score matching was performed to select 60 control patients (1:2 ratio) matched according to sex and SOFA score. For each AA genotype patient two matched patients with the GG/AG-genotype were selected from the cohort, resulting in a comparison balanced for the considered confounders. Statistical differences in protein intensities between the two groups were assessed using student’s t-test and the resulting p-values were adjusted for multiple testing using the Benjamini-Hochberg method. Missing data was not imputed, only proteins with at least five observations in each of the compared sub-cohorts were considered for statistical testing. The effect size was calculated on back-transformed data as ratio of mean intensities. Calculations were performed in R (v.4.4.3) using the MatchIt package (15). 2.6 FACS analysis/immunophenotyping EDTA-treated whole blood samples were stained with optimal concentrations of each antibody for 10 minutes at room temperature in the dark. Erythrocytes were lysed using VersaLyse (Beckman Coulter, USA) supplemented with 2.5% IOTest 3 Fixative Solution (Beckman Coulter, USA) for 30 minutes at room temperature in the dark. All samples were immediately acquired on a CytoFlex flow cytometer (Beckman Coulter, USA). Instrument performance was verified daily using CytoFlex Daily QC Fluorospheres (Beckman Coulter, USA) according to the manufacturer’s instructions. No adjustments to the compensation matrices were required throughout the study. 2.6.1 Antibodies The following fluorochrome-conjugated monoclonal antibodies were used for flow cytometric analysis. All antibodies were obtained from BioLegend (San Diego, CA, USA) unless otherwise indicated. CD4 (clone OKT4, A700, RRID: AB_571943, 1:200), CD3 (clone OKT3, BV785, RRID: AB_2563507, 1:200), CD8 (clone SK1, PeCy7, RRID: AB_2044006, 1:100). 2.7 Statistical analysis Continuous variables are presented as means ± standard deviation (SD), or standard error of the mean (SEM) when normally distributed and as medians with interquartile ranges (IQR; 25th to 75th percentile) for distributions that are not normal. Differences between groups for continuous data were determined using the t-test, Mann-Whitney U testor the Wilcoxon rank-sum test, depending on the distribution of the data. Resulting p-values were further adjusted for multiple testing using the Benjamini-Hochberg false discovery rate (FDR). The 52 p-values were ranked in ascending order, and adjusted p-values were calculated according to the Benjamini–Hochberg method using Microsoft Excel or for proteomics in R (v.4.4.3) using the MatchIt package (15). For categorical variables, differences between groups were evaluated using either the Chi-square test or Fisher’s exact test, as appropriate. The distribution of genotypes was tested for Hardy-Weinberg equilibrium to ensure that genetic variation was consistent with expected frequencies using a chi-squared test. The primary survival endpoint was 30-day all-cause mortality. Survival time was defined as the interval between study
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